Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor

SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation.

Parallel Structure from Motion for UAV Images via Weighted Connected Dominating Set

Incremental Structure from Motion (ISfM) has been widely used for UAV image orientation. Its efficiency, however, decreases dramatically due to the sequential constraint. Although the divide-and-conquer strategy has been utilized for efficiency improvement, cluster merging becomes difficult or depends on seriously designed overlap structures. This paper proposes an algorithm to extract the global model for cluster merging and designs a parallel SfM solution to achieve efficient and accurate UAV image orientation. First, based on vocabulary tree retrieval, match pairs are selected to construct an undirected weighted match graph, whose edge weights are calculated by considering both the number and distribution of feature matches. Second, an algorithm, termed weighted connected dominating set (WCDS), is designed to achieve the simplification of the match graph and build the global model, which incorporates the edge weight in the graph node selection and enables the successful reconstruction of the global model. Third, the match graph is simultaneously divided into compact and non-overlapped clusters. After the parallel reconstruction, cluster merging is conducted with the aid of common 3D points between the global and cluster models. Finally, by using three UAV datasets that are captured by classical oblique and recent optimized views photogrammetry, the validation of the proposed solution is verified through comprehensive analysis and comparison. The experimental results demonstrate that the proposed parallel SfM can achieve 17.4 times efficiency improvement and comparative orientation accuracy. In absolute BA, the geo-referencing accuracy is approximately 2.0 and 3.0 times the GSD (Ground Sampling Distance) value in the horizontal and vertical directions, respectively. For parallel SfM, the proposed solution is a more reliable alternative.

WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images

Stereo matching of high-resolution satellite images (HRSI) is still a fundamental but challenging task in the field of photogrammetry and remote sensing. Recently, deep learning (DL) methods, especially convolutional neural networks (CNNs), have demonstrated tremendous potential for stereo matching on public benchmark datasets. However, datasets for stereo matching of satellite images are scarce. To facilitate further research, this paper creates and publishes a challenging dataset, termed WHU-Stereo, for stereo matching DL network training and testing. This dataset is created by using airborne LiDAR point clouds and high-resolution stereo imageries taken from the Chinese GaoFen-7 satellite (GF-7). The WHU-Stereo dataset contains more than 1700 epipolar rectified image pairs, which cover six areas in China and includes various kinds of landscapes. We have assessed the accuracy of ground-truth disparity maps, and it is proved that our dataset achieves comparable precision compared with existing state-of-the-art stereo matching datasets. To verify its feasibility, in experiments, the hand-crafted SGM stereo matching algorithm and recent deep learning networks have been tested on the WHU-Stereo dataset. Experimental results show that deep learning networks can be well trained and achieves higher performance than hand-crafted SGM algorithm, and the dataset has great potential in remote sensing application. The WHU-Stereo dataset can serve as a challenging benchmark for stereo matching of high-resolution satellite images, and performance evaluation of deep learning models. Our dataset is available at https://github.com/Sheng029/WHU-Stereo

Hierarchical Motion Consistency Constraint for Efficient Geometrical Verification in UAV Image Matching

This paper proposes a strategy for efficient geometrical verification in unmanned aerial vehicle (UAV) image matching. First, considering the complex transformation model between correspondence set in the image-space, feature points of initial candidate matches are projected onto an elevation plane in the object-space, with assistant of UAV flight control data and camera mounting angles. Spatial relationships are simplified as a 2D-translation in which a motion establishes the relation of two correspondence points. Second, a hierarchical motion consistency constraint, termed HMCC, is designed to eliminate outliers from initial candidate matches, which includes three major steps, namely the global direction consistency constraint, the local direction-change consistency constraint and the global length consistency constraint. To cope with scenarios with high outlier ratios, the HMCC is achieved by using a voting scheme. Finally, an efficient geometrical verification strategy is proposed by using the HMCC as a pre-processing step to increase inlier ratios before the consequent application of the basic RANSAC algorithm. The performance of the proposed strategy is verified through comprehensive comparison and analysis by using real UAV datasets captured with different photogrammetric systems. Experimental results demonstrate that the generated motions have noticeable separation ability, and the HMCC-RANSAC algorithm can efficiently eliminate outliers based on the motion consistency constraint, with a speedup ratio reaching to 6 for oblique UAV images. Even though the completeness sacrifice of approximately 7 percent of points is observed from image orientation tests, competitive orientation accuracy is achieved from all used datasets. For geometrical verification of both nadir and oblique UAV images, the proposed method can be a more efficient solution.

Efficient Structure from Motion for Oblique UAV Images Based on Maximal Spanning Tree Expansions

The primary contribution of this paper is an efficient Structure from Motion (SfM) solution for oblique unmanned aerial vehicle (UAV) images. First, an algorithm, considering spatial relationship constrains between image footprints, is designed for match pair selection with assistant of UAV flight control data and oblique camera mounting angles. Second, a topological connection network (TCN), represented by an undirected weighted graph, is constructed from initial match pairs, which encodes overlap area and intersection angle into edge weights. Then, an algorithm, termed MST-Expansion, is proposed to extract the match graph from the TCN where the TCN is firstly simplified by a maximum spanning tree (MST). By further analysis of local structure in the MST, expansion operations are performed on the nodes of the MST for match graph enhancement, which is achieved by introducing critical connections in two expansion directions. Finally, guided by the match graph, an efficient SfM solution is proposed, and its validation is verified through comprehensive analysis and comparison using three UAV datasets captured with different oblique multi-camera systems. Experiment results demonstrate that the efficiency of image matching is improved with a speedup ratio ranging from 19 to 35, and competitive orientation accuracy is achieved from both relative bundle adjustment (BA) without GCPs (Ground Control Points) and absolute BA with GCPs. At the same time, images in the three datasets are successfully oriented. For orientation of oblique UAV images, the proposed method can be a more efficient solution.